Abstract: Computer-implemented method for providing corrected plant-related index data, comprising the steps of: providing initial plant-related index data for an agricultural field, preferably based on at least one satellite image; and correcting the initial plant-related index data for the agricultural field at least based on historical plant-related index data for the agricultural field and providing corrected plant-related index data.

Abstract: A method for generating a crop phenology prediction (7) is provided. The method comprises the steps of providing crop phenology training data (1) for a plurality of crops and a plurality of locations (17); training a machine learning system (6) using the crop phenology training data (1); providing a selection of the plurality of crops and a specific location (18); and generating a crop phenology prediction (7) for a selection of the plurality of crops at a specific location (18) using the trained machine learning system (6). Further, a system (21) for generating a crop phenology prediction (7) is provided.

Abstract: A computer-implemented method for verifying and/or correcting geographical map data comprising the following steps: providing geographical raw map data relating to at least one geographical area (S10); providing at least one historical temporal series f geographical map data relating to at least a part of the geographical area to which the geographical raw map data relates (S20); extracting predetermined features from the geographical raw map data and extracting features from the historical temporal series of geographical map data (S30); classifying and allocating the geographical raw map data to at least a first group of geographical map data or a second group of geographical map data based on a comparison of the features extracted from the geographical raw map data and the features extracted from the historical temporal series of geographical map data (40); executing a first operation for the first group of geographical map data and executing a second operation for the second group of geographical map data

Abstract: In a computing device and agricultural planning method the computing device receives data indicative of at least one practice associated with growing an agricultural crop. An overall sustainability score is determined for at least one sustainability category by determining an overall level for at least one indicator indicative of the sustainability category. A benchmark score is also determined for the at least one sustainability category by determining a benchmark level corresponding to the at least one indicator. The benchmark level is at least in part a function of the crop, the location of the field in which the crop is planted and the sustainability category. A comparison value of the overall sustainability score to the benchmark score for the at least one sustainability category is determined. An indicator of whether the comparison value is below a predetermined minimum threshold comparison value is output.

Abstract: In a computing device and agricultural planning method the computing device receives data indicative of at least one practice associated with growing an agricultural crop. An overall sustainability score is determined for at least one sustainability category by determining an overall level for at least one indicator indicative of the sustainability category. A benchmark score is also determined for the at least one sustainability category by determining a benchmark level corresponding to the at least one indicator. The benchmark level is at least in part a function of the crop, the location of the field in which the crop is planted and the sustainability category. A comparison value of the overall sustainability score to the benchmark score for the at least one sustainability category is determined. An indicator of whether the comparison value is below a predetermined minimum threshold comparison value is output.